WO2017183476A1

WO2017183476A1 - Information processing device, information processing method, and program

Info

Publication number: WO2017183476A1
Application number: PCT/JP2017/014451
Authority: WO
Inventors: 延浩小川; 勝吉金本; 淳史野田; 拓也藤田
Original assignee: ソニー株式会社
Priority date: 2016-04-22
Filing date: 2017-04-07
Publication date: 2017-10-26
Also published as: US20190114558A1; JPWO2017183476A1; JP7017143B2

Abstract

The present disclosure relates to an information processing device, an information processing method, and a program whereby learning data can be transferred to another agent. According to the present disclosure, when learning data is to be transferred from a user to another user, the learning data is divided into individual-dependent, private-type leanring data and the other learning data, namely, public-type learning data, and the other user is allowed to take over only the public-type learning data. Further, when learning data is to be transferred to an agent for a new vehicle, the learning data is classified into individual-vehicle-dependent data, vehicle-type-dependent data, and general purpose data, and only the general purpose data is transferred, except that when the new vehicle is of the same type as the preceding vehicle, the vehicle-type-dependent data is also transferred. The present disclosure can be applied to intelligent agents.

Description

Information processing apparatus, information processing method, and program

The present disclosure relates to an information processing apparatus, an information processing method, and a program, and more particularly, to an information processing apparatus, an information processing method, and a program that can easily inherit learning data stored in hardware.

An “intelligent agent” is known as a device that recognizes an environmental situation based on detection results from various sensors, infers the next operation, and performs an operation according to the inference result (Non-Patent Document 1). reference).

There are various intelligent agents. Specific examples include learning based on past input histories when characters are input by a user in an electronic device such as a personal computer or a smartphone. To display the most likely candidates and allow the user to select them.

In other words, since the description about the unknown environment cannot be obtained completely, the intelligent agent learns the knowledge of the unknown environment according to the experience, accumulates the learning result, repeats the inference, and operates according to the inference result. To do.

However, the accumulated learning results such as personalized history data are deleted from the viewpoint of privacy when the user is changed when the hardware with the intelligent agent is replaced. Therefore, the user who uses the new hardware cannot make use of the learning results accumulated so far with the intelligent agent of the new hardware.

Also, when a new user starts using old hardware, the learning results accumulated by the previous user are erased from the viewpoint of privacy, so there is no learning result and learning from the beginning again. Will be redone.

As a result, even if the learning results acquired by the intelligent agent in the past contain useful information for new users, the learning results up to that point cannot be used and should be obtained from the learning results. It was common not to receive the benefits of.

The present disclosure has been made in view of such a situation, and in particular, a learning result obtained by learning by an intelligent agent or the like mounted on hardware is changed by a hardware user or a new hardware is obtained. Even if it comes to use the wear, it will be able to take over in an appropriate state.

An information processing apparatus according to one aspect of the present disclosure includes a storage unit that stores learning data in association with a user who uses hardware, an operation determination unit that determines an operation of the hardware based on the learning data, Among the learning data stored in the storage unit, including a presentation unit that presents options of learning data that can be used by the user, the operation determination unit, among the options presented by the presentation unit, An information processing apparatus that determines an operation based on selected learning data.

The learning data can be composed of private data depending on the user and public data other than the private data, and the presentation unit stores the learning data stored in the storage unit Among them, the public data can be presented as options as learning data that can be used by the operation determination unit.

The learning data may be composed of individual-dependent data that depends on the individual hardware, model-dependent data that depends on the hardware model, and general-purpose data that does not depend on the hardware. In the presenting unit, when the user uses another hardware different from the hardware and causes the operation determining unit of the other hardware to determine an operation, the user is stored in association with the hardware. Among the learned data, the general-purpose data can be presented with options including private data and public data. The learning data here refers to various data acquired from the hardware when the user is using the hardware. The various data acquired from hardware includes data acquired based on the operation of each device and software included in the hardware.

When the hardware and the other hardware are the same model, the presentation unit uses the other hardware different from the hardware, and the presentation unit uses the other hardware. Among the learning data stored in association with the other hardware, when the operation is determined by the operation determination unit, the model-dependent data and the general-purpose data, which are private data and public data, respectively. It is possible to present an option consisting of

The presenting unit is configured to present the individual-dependent data, the model-dependent data, and the general-purpose data in the learning data, and options to be discarded in units of private data and public data. Can do.

The learning data can be configured for each piece of hardware used by the user, and the presentation unit has other options for selecting the hardware to which the learning data is to be transferred, and Of the learning data stored in the storage unit, public data of learning data learned by the hardware used by the user is presented as an option of learning data that can be used by the operation determination unit of the hardware. Can be.

The learning data is configured for each piece of hardware used by the user and is dependent on the individual of the hardware, dependent on the individual of the hardware, dependent on the model of the hardware, and dependent on the hardware The presenting unit is configured to include general-purpose data that is not to be transferred, among other learning data stored in the storage unit, and other options for selecting the hardware that the learning data is to be transferred. General data of learning data learned by the hardware used by the user, which is public data and private data, and learning data that can be used by the operation determination unit of other hardware different from the hardware It can be presented as an option.

The hardware may be a vehicle, and the learning data includes individual-dependent data that depends on an individual of the vehicle, vehicle-type dependent data that depends on four types of the vehicle, and general-purpose data that does not depend on the vehicle. The individual-dependent data can include repair history, mileage, remodeling information, collision history, and gasoline remaining amount, and the vehicle type-dependent data includes the vehicle type data for each vehicle type. Commonly specified fuel efficiency, maximum speed, information on whether to navigate on route to be navigated, sensing data to detect vehicle movement, information on whether to allow automatic driving, and driving route at the time of automatic driving The general-purpose data includes route information, nearby store information, visit location history, conversation history with agents, driving methods, sudden braking, Number of times of smoking, presence / absence of smoking, weather, buildings, roads, etc., and external information can be included.

Based on the learning data, it is possible to include an anonymization unit that anonymizes the data of the determined operation, and the storage unit is based on the learning data by the operation determination unit. The data of the determined operation can be stored in an anonymized state by the anonymization unit.

An information processing method according to one aspect of the present disclosure includes a storage unit that stores learning data in association with a user who uses hardware, an operation determination unit that determines an operation of the hardware based on the learning data, Among the learning data stored in the storage unit, an information processing method of an information processing apparatus including a presentation unit that presents options of learning data that can be used by the user, the operation determination unit includes the presentation unit This is an information processing method including a step of determining an action based on selected learning data among the options presented by.

A program according to an aspect of the present disclosure includes a storage unit that stores learning data in association with a user who uses hardware, an operation determination unit that determines an operation of the hardware based on the learning data, and the storage A presentation unit that presents options of learning data that can be used by the user among the learning data stored in the unit, and the action determination unit is selected from the options presented by the presentation unit This is a program for causing a computer to execute processing for determining an operation based on the learned data.

In one aspect of the present disclosure, learning data is stored in association with a user who uses hardware, the operation of the hardware is determined based on the learning data, and the learning is stored in the storage unit Among the data, learning data options that can be used by the user are presented, and an action is determined based on the selected learning data among the presented options.

According to one aspect of the present disclosure, even if a hardware user changes a learning result acquired by learning by an intelligent agent or the like installed in hardware or uses new hardware, It is possible to take over in an appropriate state.

It is a figure showing an example of composition of an agent system which consists of vehicles and a server to which this indication is applied. It is a flowchart explaining the agent process by the vehicle of FIG. It is a flowchart explaining the authentication process of FIG. It is a figure explaining the authentication process of FIG. It is a figure explaining the authentication process of FIG. It is a figure explaining the authentication process of FIG. It is a figure explaining the authentication process of FIG. It is a figure explaining the image which displays the action which can be corresponded with usable agent data. It is a figure explaining the rule which concerns on transfer of agent data. It is a flowchart explaining the use setting process of FIG. It is a figure explaining the use setting process of FIG. It is a flowchart explaining the transfer setting process of FIG. It is a figure explaining the migration setting process of FIG. It is a figure explaining the migration setting process of FIG. It is a flowchart explaining the discard setting process of FIG. It is a figure explaining the discard setting process of FIG. It is a flowchart explaining the agent function execution process of FIG. And FIG. 11 is a diagram illustrating a configuration example of a general-purpose personal computer.

Hereinafter, preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In addition, in this specification and drawing, about the component which has the substantially same function structure, duplication description is abbreviate | omitted by attaching | subjecting the same code | symbol.

<Example configuration of an agent system consisting of a vehicle equipped with an agent and a server that manages agent data>
FIG. 1 illustrates a configuration example of an agent system including a vehicle on which an agent to which an information processing apparatus according to the present disclosure is applied and a server that manages agent data.

The vehicles 11-1 to 11-n in FIG. 1 are equipped with an agent that executes processing according to a request of a user who is a driver. The agent, for example, provides various driving support to the user who uses the vehicles 11-1 to 11-n. For example, the navigation function for guiding the user to the destination and the fuel efficiency are improved. It realizes driving support functions such as guiding accelerator work and brake work.

In the present embodiment, the agent functions as a navigation device that searches and guides a route (route) to a desired destination, particularly when the user who is a driver uses the vehicle 11. However, other functions may be realized.

In the following, the vehicles 11-1 to 11-n are simply referred to as the vehicle 11 unless otherwise distinguished, and the other configurations are also referred to in the same manner.

In this case, in addition to general route search using map information, the agent searches for routes that exclude difficult routes that are narrower than the vehicle width, for example, from vehicle size information. To do. In addition, for example, when a predetermined destination is set in a specific time zone, the agent sets the destination at a specific time when there is a history of frequently set waypoints (stop points). Just search for routes that include frequently set stops. In other words, when the route search to the destination desired by the driver, the agent repeats learning according to the driver's habit and history related to the route search and the conditions of the vehicle 11, and performs the route search according to the learning result. By executing, a route search optimum for the driver is executed.

More specifically, the agent stores the learning result as agent data in association with a driver or a vehicle and stores it in the server 12 including a cloud server or the like via the network 13 represented by the Internet. The agent authenticates the driver, accesses the server 12 based on the authentication result, reads out the corresponding agent data, and uses it for route search and the like.

Since this agent data is associated with the driver and the vehicle, for example, even when the driver P changes from the vehicle A to the vehicle B, the agent data used according to the driver is transmitted from the vehicle A to the vehicle B. It can be used by migrating to. At this time, the search result of the route search included in the agent data, the learning result of the history, and the like can be transferred as they are. For example, the size of the vehicle is the one of the new vehicle B.

Furthermore, as agent data associated with the vehicle A, for example, a history of traveling on the top of Mt. Fuji, a special history indicating traveling in a special area such as driving through the North American continent, etc. are specified as personal information. It is possible to make it in a state where it can be recognized when a new driver Q takes over the vehicle A and drives it. Further, in the agent data associated with the vehicle A, the repair history, accident history, etc. of the vehicle A can also be left as a special history. The special history included in the agent data can be used as value-added information of the vehicle A.

That is, among these special histories, a history of traveling on the top of Mt. Fuji and a special history indicating traveling in a special area such as driving through the North American continent enhance the premium feeling for the vehicle A (increase added value). ) Can be used as information. Of the special history, repair history, accident history, and the like can be used as information for reducing the added value to the vehicle A.

As a result, when the vehicle A is sold, how much the vehicle history such as the mileage, the number of years of use, the repair history, and the accident history included in the special history lowers the added value of the vehicle A. However, among the special histories, a history that increases with a premium feeling (increases added value) can be an index that increases the value of the vehicle A.

Here, a configuration example of the vehicle 11 will be described.

The vehicle 11 includes a control unit 31, a vehicle drive unit 32, an operation input unit 33, a vehicle operation detection unit 34, a communication unit 35, a display unit 36, an audio output unit 37, an audio input unit 38, an imaging unit 39, a storage unit 40, And an agent processing unit 41.

The control unit 31 is a computer composed of a so-called ECU (Engine Control Unit) or the like, and controls the entire operation of the vehicle 11.

The vehicle drive unit 32 is a general term for drive parts included in the vehicle 11 such as an engine, an accelerator, a brake, an air conditioner (air conditioner), and lighting, and the operation of the drive unit 32 is controlled by the control unit 31. The control unit 31 may control the automatic driving of the vehicle 11, and in this case, the automatic driving is realized by controlling the vehicle driving unit 32.

The operation input unit 33 is a button or a touch panel for inputting various information for the agent controlled by the agent control unit 41, and is operated by a driver as a user to output an operation signal corresponding to the operation content.

The vehicle motion detection unit 34 includes various sensors in the vehicle 11 and detects the presence or absence of an automatic brake operation, the presence or absence of a collision, in addition to the yaw, pitch, and roll by a three-dimensional acceleration sensor.

The communication unit 35 includes an Ethernet (registered trademark) board and the like, and communicates with the server 12 via the network 13 to transmit and receive various data.

The display unit 36 is a display composed of an LCD (Liquid Crystal Display), an organic EL (Electro Luminescence), or the like, and displays various types of information.

The audio output unit 37 includes a speaker and outputs various types of information as audio. That is, the voice output unit 37 outputs, for example, information related to route guidance in the car navigation function of the agent realized by the function of the agent processing unit 41 as a voice.

The voice input unit 38 is composed of a microphone or the like, and accepts the driver's instruction content by voice as with the operation input unit 33.

The imaging unit 39 is configured by an imaging device such as a CMOS (Complementary Metal Oxide Semiconductor) and captures the traveling direction of the vehicle 11, the rear, the rear of the side, the lower side, the interior of the vehicle, the entire periphery of the vehicle, and the facial expression of the driver Output the image signal.

The storage unit 40 includes a flash memory and the like, reads and stores the agent data stored in the server 12, and is appropriately rewritten according to the processing of the agent processing unit 41. The agent data stored in the storage unit 40 is stored in the server 12 as necessary.

The agent processing unit 41 realizes the above-described function as an agent. The agent processing unit 41 includes an authentication unit 61, an account management unit 62, an agent data synchronization management unit 63, an agent data use management unit 64, an agent data migration management unit 65, an agent data discard management unit 66, an analysis unit 67, and a case search unit. 68, a case confirmation unit 69, a case correction unit 70, a case determination unit 71, a case anonymization unit 72, and a case verification unit 73.

When the user who is a driver gets on the vehicle 11, for example, the authentication input unit 61 authenticates whether or not the operation input unit 33 is operated and the user is registered using the input ID and password. To do. The authentication unit 61 may authenticate whether or not the user is a registered driver based on the driver's face image captured by the imaging unit 39, for example. In addition to this, the authentication unit 61 may perform authentication using a user's retina pattern or fingerprint imaged by the imaging unit 39, or a user's voiceprint input via the audio input unit 38. You may make it authenticate using.

The account management unit 62 manages an account of a user who is a driver that manages agent data related to the vehicle 11. More specifically, the account management unit 62 registers a user who is a driver as an account when authentication is not permitted by the authentication unit 61, that is, a new driver. In addition, an account is normally set for the vehicle 11 in association with a driver who is a user who is an individual owner.

If the next owner does not exist, such as when the vehicle 11 is sold, the account does not exist. However, data dependent on the vehicle 11 such as the travel distance and repair information of the vehicle 11 needs to be managed in association with the vehicle 11 even in the absence of a driver account. Therefore. In such a case, the account management unit 62 sets a temporary account, manages the individual-dependent data in the server 12 in association with the temporary account even when the driver is not registered, and registers a driver as a new owner. May be managed. In this case, the temporary account to be registered for the new driver is deleted, and the server 12 is managed in association with the account for which the driver is set.

The agent data synchronization management unit 63 accesses the server 12 based on the authentication result and information for identifying the vehicle 11, and synchronizes the agent data of the authenticated user and stored in association with the vehicle 11. Then, it is stored in the reading storage unit 40.

The agent data usage management unit 64 executes usage setting processing of the agent data stored in the storage unit 40, and performs usage settings according to the operation content of the operation input unit 33.

When the user changes to the new vehicle 11 due to replacement of the vehicle 11 or the like, the agent data transfer management unit 65 executes a transfer setting process for the agent data to the new vehicle 11 and sets the operation content of the operation input unit 33. Make the appropriate migration settings.

The agent data discard management unit 66 executes a discard setting process, accepts an operation by the operation input unit 33, and performs discard setting of data instructed to discard among the agent data.

When the agent data use management unit 64 uses the agent data stored in the storage unit 40 to execute the function as an agent, the analysis unit 67 performs the operation input unit 33, the vehicle motion detection unit 34, and the imaging. Based on information of various detection results supplied from the unit 39, the situation of the vehicle 11 is analyzed, and a case that becomes a problem is specified.

The case search unit 68 searches for cases analyzed by the analysis unit 67 from the cases stored as agent data, and outputs the searched cases as target cases.

The case confirmation unit 69 confirms whether or not the problem identified by the analysis unit 67 is solved based on the searched target case.

The case correction unit 70 corrects the searched target case so that the problem can be solved when the problem cannot be solved in the case retrieved by the case confirmation unit 69.

The case determination unit 71 determines whether or not the problem has been solved by agent processing according to the target case, and the personal information of the driver is specified in managing the target case that has been solved as agent data. It is determined whether or not anonymization is necessary so as not to be performed.

The case anonymization part 72 anonymizes the information of the case which was able to solve a subject, for example by k anonymization, when the case determination part 71 considers that anonymization is required.

The case verification unit 73 verifies whether or not the case that can solve the problem is a falsified case.

The agent data use management unit 64 stores case data that has been solved by an anonymization as necessary and has not been tampered with as agent database in the agent data. Then, the agent data use management unit 64 performs the function as an agent using the agent data in which the cases are accumulated in the subsequent processing. As a result, by accumulating cases, learning for demonstrating the function as an agent is repeated, and the processing accuracy is improved.

Note that the term “learning” used here refers to learning and correction of agent data to solve a problem by using agent data, which is learning data necessary for exhibiting the agent function, and solves the problem. In addition to executing the process, the process of storing the executed content as agent data is repeated.

Agent data, which is learning data, uses not only the processing contents (examples) executed to solve a problem by using search results and correction results for solving the problem, but also the user's hardware. It includes various data acquired from hardware when in use.

For example, when the hardware is a vehicle, the various data includes the location and time of sudden braking, the user's driver status (wake, sleeping, heart rate, etc.), staggered driving, and slipping. Location, time and driver status, lane departure location, time and driver status, signal ignorance, overtaking location, time and driver status, and accident location, time and driver status, etc. is there. That is, when the hardware is a vehicle, the agent data, which is learning data, includes data indicating the operating state of the vehicle, which is hardware, and the driver's action in addition to the contents executed to solve the problem. It can also be said that it includes data to be shown, that is, an operation history.

Furthermore, the data indicating the hardware operation state is data obtained by executing software that controls the hardware operation, or data obtained from the outside to control the hardware operation, for example, a cloud Includes data obtained from the server.

The server 12 is constructed by at least one or more servers on the network 13 such as a cloud server, and stores agent data of the vehicles 11-1 to 11-n. A communication unit 92 and a storage unit 93 are provided.

The control unit 91 controls the entire operation of the server 12. The communication unit 92 includes, for example, an Ethernet (registered trademark) board, communicates with the vehicle 11 via the network 13, and transmits and receives various data. The storage unit 93 stores agent data registered in association with at least one of the vehicle 11 and the user.

<Agent processing>
Next, agent processing by the agent system of FIG. 1 will be described with reference to the flowchart of FIG.

In step S11, the authentication unit 61 determines whether the driver has boarded based on information supplied from the operation input unit 33, the vehicle motion detection unit 34, and the imaging unit 39. That is, for example, when the operation input unit 33 is operated and information indicating boarding is input, for example, the operation is unlocked by the vehicle operation detection unit 34, the door is released, and the operation state is set. When it is detected that the image has been made, or when the face image is detected in the vicinity of the driver's seat, for example, in the vehicle image captured by the imaging unit 39, the authentication unit 61 assumes that the driver has boarded The processing proceeds to step S12. In addition, the process of step S11 is repeated until boarding is detected.

In step S12, the authentication unit 61 executes an authentication process to authenticate the driver.

<Authentication process>
Here, the authentication process will be described with reference to the flowchart of FIG.

In step S41, the authentication unit 61 displays an authentication image start image as shown in FIG. 4, for example.

In FIG. 4, a “login” image is displayed, and a button B1 that is operated when inputting an ID displayed as “ID input” is displayed at the upper center. In addition, below it is displayed, “Please perform the specified operation if you are using fingerprint authentication / device authentication”, which can be used other than the method of entering ID and password. For example, fingerprint It is instructed to perform a predetermined operation corresponding to the case of using authentication or device authentication using a face image.

For example, when the button B1 is operated, as shown in FIG. 5, an ID input column C1 displayed as “ID”, a password input column C2 displayed as “Password”, and a keyboard C3 are displayed. The ID input field C1 and the password input field C2 are selected with a pointer or the like, and the keyboard and C3 are operated with the operation input unit 33 so that the ID and password can be input.

Furthermore, after the ID and password are entered, a button C4 is displayed which displays “Completed” indicating that it has been completed.

In step S42, the authentication unit 61 determines whether an ID and a password have been input, and the processes in steps S41 and S42 are repeated until it is determined that the ID and password have been input. In step S42, for example, the ID input field C1 and the password input field C2 are selected, the keyboard C3 is operated by the operation input unit 33, the ID and password are input, and the button C4 is operated. Thus, it is considered that the ID and password have been input, and the process proceeds to step S43.

In step S43, the authentication unit 61 controls the communication unit 35 to collate with the server 12 whether the pre-registered ID and password match. At this time, for example, an image displayed as “logged in” as shown in FIG. 6 is displayed.

In response to this, when the control unit 91 of the server 12 controls the communication unit 92 to acquire the transmitted ID and password, is the user ID and password registered in the storage unit 93 in advance? Check whether or not. And the control part 91 controls the communication part 92, and transmits a collation result to the vehicle 11 which has transmitted ID and a password.

In step S44, the authentication unit 61 determines whether the ID and the password match and the authentication is permitted based on the collation result.

In step S44, if authentication is permitted (authentication is OK), in step S45, the authentication unit 61 recognizes that authentication is permitted.

On the other hand, if authentication is not permitted (authentication is NG) in step S44, the authentication unit 61 recognizes that authentication is not permitted in step S46.

The above process completes the authentication process.

Here, the description returns to the flowchart of FIG.

When the authentication process is completed in step S12, the process proceeds to step S13.

In step S13, the authentication unit 61 determines from the authentication result whether authentication is permitted (authentication is OK) or not.

In step S13, if authentication is not permitted, the process proceeds to step S24.

In step S24, the authentication unit 61 determines whether or not the operation input unit 33 is operated to request setting of a new account. If it is determined in step S24 that setting of a new account is requested, the process proceeds to step S25.

In step S25, the account management unit 62 controls the display unit 36 to display an image for setting a new account, and accepts an operation input for setting a new account by the operation input unit 33.

In step S <b> 26, the account management unit 62 sets a new account based on the information input by operating the operation input unit 33 and controls the communication unit 35 to make a new account for the server 12. The account is registered, and the process proceeds to step S23.

Further, when setting of a new account is not requested in step S24, in step S27, the authentication unit 61 confirms that authentication is not permitted (authentication is NG), for example, as shown in FIG. The display unit 36 is controlled and displayed, and the process proceeds to step S23. In FIG. 7, “Login failed” is displayed, indicating that authentication has not been accepted.

In step S23, the authentication unit 61 determines whether or not the driver has got off based on information supplied from the operation input unit 33, the vehicle motion detection unit 34, and the imaging unit 39. If it is determined in step S23 that the driver has got off, the process returns to step S12.

That is, when the boarding is permitted but the authentication is not permitted, a new account is set, and the process of steps S12, S13, S23 to S26 is repeated until the authentication is permitted, and the user gets off the vehicle 11 Until this is done, the agent processing cannot continue.

If the authentication is approved in step S13, the process proceeds to step S14.

In step S <b> 14, the agent data synchronization management unit 63 controls the communication unit 35 to read out agent data of a driver (user) whose authentication is permitted from the server 12 and stores the agent data in its own storage unit 40. The agent data is updated (synchronized with the agent data of the server 12).

In step S15, the agent data synchronization management unit 63 selects the usable data and the corresponding action as shown in FIG. 8, for example, based on the agent data stored in the storage unit 40. Is generated and the display unit 36 is controlled and displayed.

In FIG. 8, data of the user authenticated from above, data already applied to the vehicle A, and data associated with the user A managed by the server 12 are displayed.

FIG. 8 shows the agent data of user A as authenticated user data. In addition, individual-dependent data (Public), vehicle-type dependent data (Public), and general-purpose data (Public) are shown as data already applied to the vehicle A, which is the vehicle 11 that is executing a series of processing of the agent. That is, it is indicated that the data already applied to the vehicle A is individual-dependent data (Public), vehicle-type dependent data (Public), and general-purpose data (Public).

Further, it is shown that the data associated with the user A includes data when driving the vehicle B and data when driving the vehicle C. Data (Public / Private) and general-purpose data (Public / Private) are shown, and data when driving the vehicle B is individual-dependent data, vehicle-type-dependent data (Public / Private), and general-purpose data (Public / Private) It has been shown.

In addition, individual-dependent data, vehicle-type dependent data, and general-purpose data (Public / Private) are shown when driving the vehicle C, and data when driving the vehicle B is individual-dependent data, vehicle-type-dependent data, general-purpose data ( Public / Private).

Further, at the bottom of FIG. 8, from the left, a button 111 labeled “use”, “move”, which is operated when selecting each of the agent data use setting, transfer setting, and discard setting, A button 112 indicated and a button 113 indicated as “Discard” are provided.

<Configuration of agent data>
Here, the configuration of the agent data will be described with reference to FIG.

The agent data consists of three types of data: individual-dependent data, vehicle-type-dependent data, and general-purpose data. Furthermore, for each data, there are a private type that includes user-dependent information and other public types. is there.

Therefore, for the agent data, the private type and the public type are set for each of the three types of individual-dependent data, vehicle-type dependent data, and general-purpose data, and there are a total of six types of data.

Individual-dependent data is data specific to an individual (in this case, a vehicle) on which an agent is installed (refers to the hardware itself). More specifically, since the hardware is a vehicle, the individual-dependent data includes, for example, repair history, travel distance, modification information, collision history, and gasoline remaining amount.

The vehicle type-dependent data is data that is common not only to the individual vehicle but also to all types of vehicle vehicles that are hardware. More specifically, since the vehicle type dependent data is the vehicle, the fuel consumption, the maximum speed, the information on whether or not to pass on the route to be navigated, Sensing data (for example, automatic brake activation conditions), automatic driving availability information, travel route during automatic driving, and the like.

General-purpose data is general-purpose data that does not depend on the vehicle that is hardware. In more detail, since the hardware is a vehicle, the general-purpose data is, for example, route information, nearby store information, visit place history, conversation history with agents, driving method, sudden braking, number of horns, presence or absence of smoking 3D data such as weather, buildings, and roads, and external information.

Private type data is information that depends on the driver who is the user of the vehicle, which is hardware, but may not be information that can be specified by an individual. If time is included, it is Private type data.

Furthermore, Public type data is data that is not Private type data. For example, it is not possible to specify simple route information or a place or time, that is, a driving history that cannot specify which user belongs. It is.

In FIG. 9, for each type of agent data, each upper left part indicates whether or not it is possible to migrate to a different agent, and each lower right part indicates whether or not it is possible to migrate to a different user. Here, the circle mark indicates that transfer is possible, the cross mark indicates that transfer is not possible, and the triangle mark indicates that transfer is possible if the vehicle type is the same.

The agent data is transferred to a different agent because the agent is different for each vehicle 11. Therefore, the agent data used when the same user drives a different vehicle A is transferred to the agent of a different vehicle B. Use different agents.

Further, to transfer the agent data to a different user means to use the agent by transferring the agent data of the user B to the agent of the vehicle 11 used by the user A when the different user B drives.

That is, individual-dependent data cannot be transferred to a different agent for both Public type and Private type data.

Also, as for the vehicle type dependent data, both public type and private type data can be transferred to different agents if the vehicle type is the same, but cannot be transferred to different agents if the vehicle type is different.

Furthermore, general-purpose data can be transferred to different agents for both public and private data.

All private-type data, such as individual-dependent data, vehicle-type-dependent data, and general-purpose data, may contain personal information and are user-specific, and therefore cannot be transferred to different users.

Also, since all public type data such as individual-dependent data, vehicle type-dependent data, and general-purpose data are not user-specific, they can be transferred to different users.

Here, the description returns to the display example of FIG. That is, since the agent data is configured as described above, the individual-dependent data (Public), the vehicle-type-dependent data (Public), and the general-purpose data (Public) listed as data already applied to the vehicle A in FIG. Is data associated with the vehicle A, and since the authenticated user A has never driven the vehicle A until now, all of the data can be transferred to different users. Because there is, it is said that it has been applied.

In addition, as data associated with the user A and when driving the vehicle B, individual-dependent data, vehicle-type dependent data (Public / Private), and general-purpose data (Public / Private) are shown. The vehicle type-dependent data (Public / Private) and general-purpose data (Public / Private) to which “Public / Private)” is attached are data that can be migrated as shown in FIG. Therefore, as shown in FIG. 9, individual-dependent data without “(Public / Private)” cannot be transferred.

That is, here, since the vehicle A is the same vehicle type as the vehicle B, it is indicated that the vehicle type dependent data (Public / Private) can be transferred.

Furthermore, the data associated with the user A, which is individual-dependent data, vehicle-type-dependent data, and general-purpose data (Public / Private) is shown as when driving the vehicle C, but “(Public / Private)” The general-purpose data (Public / Private) to which is attached indicates that the data can be migrated as shown in FIG. Therefore, as shown in FIG. 9, individual-dependent data and vehicle type-dependent data that are not attached with “(Public / Private)” cannot be transferred.

Here, it is shown that the vehicle type dependent data cannot be transferred because the vehicle A is not the same vehicle type as the vehicle C.

Here, the description returns to the flowchart of FIG.

In step S <b> 16, the agent usage management unit 64 determines whether the operation input unit 33 is operated and the button 111 labeled “USED” in FIG. 8 is pressed to instruct the usage setting of the agent data. To do.

If it is determined in step S16 that usage setting has been instructed, in step S17, the agent usage management unit 64 executes usage setting processing for agent data, and the process proceeds to step S23. Details of the usage setting process will be described later with reference to the flowchart of FIG.

On the other hand, if it is determined in step S16 that usage setting is not instructed, the process proceeds to step S18.

In step S18, the agent migration management unit 65 determines whether or not the operation input unit 33 has been operated and the button 112 labeled “migration” in FIG. To do.

If it is determined in step S18 that migration setting has been instructed, in step S19, the agent migration management unit 65 executes migration setting processing for setting migration of agent data, and the processing proceeds to step S23. Details of the migration setting process will be described later with reference to the flowchart of FIG.

On the other hand, if it is determined in step S18 that the migration setting is not instructed, the process proceeds to step S20.

In step S20, the agent discard management unit 66 determines whether or not the operation input unit 33 is operated and the button 113 labeled “Discard” in FIG. To do.

If it is determined in step S20 that the discard setting has been instructed, in step S21, the agent discard management unit 66 executes a discard setting process for setting the discard of the agent data, and the process proceeds to step S23. Details of the discard setting process will be described later with reference to the flowchart of FIG.

On the other hand, when it is determined in step S20 that the discard setting is not instructed, the process proceeds to step S22.

In step S22, the analysis unit 67 executes an agent function execution process to execute the agent function, and proceeds to step S23. The agent function execution process will be described in detail later with reference to the flowchart of FIG.

The agent process is executed by the series of processes described above, and the vehicle 11 can be operated using the agent function. In addition, agent data required for using the agent function can be transferred to another user or used for use in another vehicle.

<Usage setting process>
Next, usage setting processing will be described with reference to the flowchart of FIG.

In step S71, the agent data use managing unit 64 displays an image for setting use of agent data as shown in FIG. 11, for example. Here, in FIG. 11, “applied” is written on the right side of each of the individual-dependent data (Public), the vehicle-type dependent data (Public), and the general-purpose data (Public) as data already applied to the vehicle A. Buttons 131 to 133 are displayed, and each time the button is pressed, “Applied” indicating that it is applicable but not applied and “Applied” indicating that it is already applied are sequentially switched. Displayed and set to a state corresponding to the displayed state. In FIG. 11, since it is set to be applied by default, the buttons 131 to 133 are described as “applied”.

Data that can be transferred to the vehicle A from the server 12 associated with the user A includes individual-dependent data that is displayed in gray indicating that the vehicle B cannot be used, and vehicle type-dependent data ( Public / Private) and general-purpose data (Public / Private) have

buttons

134 and 135 labeled “Apply” as described above.

Furthermore, in the data associated with the user A that can be transferred from the server 12 to the vehicle A, the individual-dependent data displayed in gray indicating that the data cannot be used as the vehicle C driving, and the vehicle type-dependent In the data and general-purpose data (Public / Private), a button 136 labeled “Apply” is displayed as described above.

In step S72, the agent data utilization managing unit 64 determines whether or not the operation input unit 33 is operated and the buttons 131 to 135 are operated. If it is determined that the operation is performed, the process proceeds to step S73. .

In step S73, the agent data utilization management unit 64 updates the agent data stored in the storage unit 40 in accordance with the operation content of the operation input unit 33 for the buttons 131 to 135.

In step S74, the agent data usage management unit 64 determines whether or not the operation input unit 33 has been operated to instruct the end of the usage setting. If termination is not instructed, the process returns to step S71, and the subsequent processes are repeated.

In step S72, when the operation input unit 33 is not operated, the process of step S73 is skipped.

That is, the processing of steps S71 to S74 is repeated until the end of use setting is instructed. In step S74, when the end is instructed, the use setting is ended.

Through the above processing, it is possible to set whether to apply the applicable data among the agent data.

<Migration setting process>
Next, the migration setting process will be described with reference to the flowchart of FIG.

In step S101, the agent data migration management unit 65 displays an image for setting migration of agent data as shown in FIG. 13, for example. In FIG. 13, “Vehicle D (February 9, 2016, 16:00) for selecting the vehicle 11 that can be selected as the migration destination is described from above. Buttons 151 to 153 on which “Scheduled to use. AA taxi”, “Vehicle E (owned by friend B)” and “Vehicle F (owned by user A)” are displayed. Since the items below that are the same as those in FIG. 8, the description thereof is omitted.

That is, buttons 151 to 153 that can select the vehicles D, E, and F that can be used by the authenticated user and can transfer the agent data are displayed.

In step S102, the agent data migration management unit 65 determines whether the operation input unit 33 is operated and the buttons 151 to 153 are operated.

In step S102, for example, when the button 151 is pressed, it is considered that the button 151 has been operated, and the process proceeds to step S103.

In step S103, the agent data migration management unit 65 updates the agent data stored in the storage unit 40 in accordance with the operation content of the operation input unit 33 for the buttons 151 to 153.

That is, when the button 151 is pressed, the agent data migration management unit 65 switches the display so as to indicate that the button 151 is selected as shown in FIG. Buttons 171 to 173 labeled “Migration” that can select whether or not the general data (Public) and general data (Public / Private) of the vehicles B and C are migrated are described. Here, since the vehicle D is not the same as any of the vehicles A, B, and C, it is indicated that the transfer of the vehicle type-dependent data (Public / Private) is not permitted.

In step S104, the agent data migration management unit 65 determines whether or not the operation input unit 33 has been operated to instruct the end of the use setting. If termination is not instructed, the process returns to step S101, and the subsequent processes are repeated.

In this case, in step S102, it is determined again whether or not there is an operation input in the state displayed as shown in FIG. 14, and for example, if any of the buttons 171 to 173 is operated, there is an operation input. The process proceeds to step S103 again.

In step S103, the agent data migration management unit 65 sets the data migration of any of the buttons 171 to 173 for which the operation input has been made, and the process proceeds to step S104.

Then, in step S104, when the migration setting end is instructed, the migration setting ends.

With the above processing, the vehicle A corresponding to at least one of the buttons 171 to 173 selected from the vehicle 11 of “vehicle D (planned to be used from 16:00 on February 9, 2016, AA taxi)” Agent data is set so that applied data, general data (Public / Private) of vehicles B and C, and general data (Public / Private) are transferred.

It should be noted that the rules in the migration process of individual-dependent data, vehicle-type-dependent data, general-purpose data, and private type and public type data in the agent data may be other than those described with reference to FIG. Good.

For example, instead of the one in FIG. 9, as the change pattern 1, the user may be able to change the circle mark, the triangle mark, and the cross mark in FIG. 9. However, it is possible to change the direction from a circle to a triangle, and from a triangle to a cross, but not vice versa.

Suppose that the change pattern 2 allows the user to change the type of individual-dependent data, vehicle-type dependent data, and general-purpose data in the figure. However, it is possible to change from general-purpose data to vehicle-type dependent data, and from vehicle-type dependent data to individual-dependent data, but not vice versa.

Furthermore, as change pattern 3, it is assumed that the user can change the type of Private type and Public type in the figure. However, it is possible to change from Public type to Private type, but not vice versa.

In other words, data that can be migrated may be incapable of migrating, but by making non-migratable data non-migratable, it is possible to keep privacy while maintaining flexibility in the rules in the migration process. It becomes. In other words, when changing the Public type to the Private type, it was possible to migrate in the first place. Even if this is impossible, privacy will not be affected, but if it is reversed, privacy will be made public and privacy considerations will be lacking. Therefore, such processing is not possible.

<Discard setting process>
Next, the discard setting process will be described with reference to the flowchart of FIG.

In step S121, the agent data discard management unit 66 displays, for example, an image for setting discard of agent data as shown in FIG. Here, in FIG. 16, individual-dependent data (Private), vehicle-type-dependent data (Private), and general-purpose data (Private) as data already applied to the vehicle A, and individual-dependent data (Private) Public / Private), vehicle-dependent data (Public / Private), general-purpose data (Public / Private), and individual-dependent data (Public / Private), vehicle-dependent data (Public / Private) , And general data (Public / Private) are provided with buttons 191 to 199 which are displayed as “discard” and which are operated when instructing the discard.

That is, since the vehicle A and the user A are not registered so as to be associated with each other, the data already applied to the vehicle A includes individual-dependent data (Private), vehicle-type-dependent data (Private), and general-purpose data (Private ), And none of them has the authority to discard (Public).

In step S122, the agent data discard management unit 66 determines whether or not the operation input unit 33 has been operated and the buttons 131 to 135 have been operated. If it is determined that the operation has been performed, the process proceeds to step S123. .

In step S123, the agent data discard management unit 66 updates the agent data stored in the storage unit 40 in accordance with the operation content of the operation input unit 33 for the buttons 191 to 199.

In step S124, the agent data discard management unit 66 determines whether or not the operation input unit 33 has been operated to instruct the end of the use setting. If termination is not instructed, the process returns to step S121, and the subsequent processes are repeated.

In step S122, when the operation input unit 33 is not operated, the process of step S123 is skipped.

That is, the processing of steps S121 to S124 is repeated until the end of discard setting is instructed. Then, in step S124, when termination is instructed, the discard setting is terminated.

With the above processing, it is possible to set whether to discard the agent data that can be discarded.

<Agent function execution processing>
Next, an agent function execution process using the agent data set by the above-described process will be described with reference to the flowchart of FIG.

In step S <b> 141, the analysis unit 67 detects the vehicle motion detection result, the input sound, and the captured image supplied from the vehicle motion detection unit 34, the voice input unit 38, and the imaging unit 39 in the vehicle 11. Accepts supply of various detection results.

In step S142, the analysis unit 67 analyzes the received various detection results, sets a case that is a problem corresponding to the analysis result as a target case, and supplies the case to the case search unit 68. For example, the analysis unit 67 performs language analysis, semantic analysis, and the like on the voice input to the voice input unit 38 when the driver as a user utters voice including a destination for instructing navigation. Thus, it is recognized that searching for a route (route) is a problem, and the search for the route to the destination is set as a target case and supplied to the case search unit 68.

In step S143, the case search unit 68 searches for a case serving as a process for solving the target case based on the agent data stored in the storage unit 40. For example, the case search unit 68 searches for route candidates R1, R2,... Rn to be selected from the past route search history included in the agent data and outputs them as case search results.

In step S144, the case confirmation unit 69 confirms whether or not the target case that is the problem can be solved from the data that is the case search result.

For example, in the route search result R1, as a route from the starting point X to the destination Y, a route from the past case to the destination Y to the starting point X, and a route from the starting point X to the waypoint A When R1, route R2 from route A to route B, and route R3 from route B to destination Y are found as search results, for example, routes R1 and R3 have no problem. The route R2 is a route when the user A has used the vehicle H in the past. The vehicle A currently driven by the user A is wider than the vehicle H, and the vehicle width is larger than the road R2. When handling is difficult, it is determined from the case search result that the target case cannot be solved. In step S144, when it is determined that the problem cannot be solved in the target case as described above, the process proceeds to step S145.

In step S145, the case correction unit 70 corrects the case search result so that the target case can be solved. That is, in the case search result described above, the route R2 is corrected to the route R2 ′ by searching for a route that can correspond to the vehicle width of the vehicle A using the agent data, and as a result, By using the routes R1, R2 ′, R3 as the route from the starting point X to the destination Y, the problem can be solved in the target case. However, even if correction is made in this way, the target case may not necessarily be resolved.

In step S146, the case determination unit 71 executes a case search result for solving the target case set in this way. That is, in this example, the case determination unit 71 controls the display unit 36 to display a navigation image up to the destination Y, and also controls the audio output unit 37 to guide voice to the destination Y. Is output.

If the target case can be solved in step S144, the process in step S145 is skipped. That is, in this case, the first case search result is used as it is to solve the target case.

In step S147, the case determination unit 71 determines whether the problem of the target case has been solved based on the case search result in a series of operations. That is, in this case, the case determination unit 71 determines whether or not the vehicle 11 can be moved from the departure point X to the destination Y by navigation based on the case search result.

In Step S147, when the target case can be solved, in Step S148, the case determination unit 71 recognizes the case search result as a successful case.

If the target case cannot be solved in step S147, the case determination unit 71 recognizes the case search result as a successful case in step S149.

In step S150, the case anonymization unit 72 determines whether or not anonymization is necessary for the case search result. For example, in the case search result, there is a possibility that information including personal information of the user A is left as private type general-purpose data that the user A has moved from the departure place X to the destination Y at a predetermined date and time. is there. Therefore, in the case where such personal information is left, the case anonymization unit 72 determines that anonymization is necessary to use the case search result as public type general-purpose data, The process proceeds to step S151.

In step S151, the case anonymization unit 72 anonymizes the case search result. Anonymization is, for example, anonymized by k anonymization processing. Here, k anonymization processing means that, for example, user A uses information on a plurality of similar case search results so that at least k people or more have moved from departure point X to destination Y. This is a process of converting to unspecified information. In other words, the k anonymization process is to convert the data attribute attached to the information that the vehicle has moved from the departure point X to the destination Y so that the user A is not specified.

For example, the case anonymization unit 72 controls the communication unit 35, accesses the server 12, searches for information that has moved from the departure place X to the destination Y, and obtains a plurality of case search results of k or more people. Ask. The case anonymization unit 72 includes information that the user A has moved from the departure point X to the destination Y on a predetermined date D, and a plurality of k or more persons have determined a predetermined year (including the predetermined date D). The information is converted into information of k or more people who have moved from the starting point X to the destination Y.

Thus, since the date and time when this data was generated is ambiguous information, the information that the user A is not specified can be determined only by “information that the vehicle has moved from the departure place X to the destination Y”.

For the case search result anonymized in this way, the case anonymization unit 72 changes the attribute of the private type general-purpose data as public type general-purpose data. By accumulating public-type general-purpose data that has been attribute-converted in this way, it is possible to search for cases with higher accuracy by increasing the data that can be referred to when searching for routes in other agents. Become.

In step S150, for example, when the case search result can be regarded as public type general-purpose data, anonymization is regarded as unnecessary, and the process in step S151 is skipped.

In step S152, the case verification unit 73 determines whether or not the case search result including public type general-purpose data is falsified data. If it is determined that the data is not falsified, in step S153, The data is stored as agent data stored in the storage unit 40 and also stored in the storage unit 93 of the server 12. At this time, the case verification unit 73 stores, in the agent data, information on whether the target case is a successful case or a failure case, with respect to the case search result including public type general-purpose data.

In step S152, if it is determined that the case search result including the public type general-purpose data is falsified, the process in step S153 is skipped, and the case search result is the public type general-purpose data. Not registered as data.

In step S154, the analysis unit 67 determines whether or not the operation input unit 33 is operated to instruct the end of the agent function execution process. If the end is not instructed, the process returns to step S141. The subsequent processing is repeated. In step S154, if an end instruction is given, the process ends.

Through the above processing, even when a vehicle having an agent function is changed, the agent data accumulated by the agent function can be taken over. That is, when the user A changes from the vehicle A to the vehicle B, that is, from the agent of the vehicle A to the agent of the vehicle B, among the agent data accumulated in the vehicle A, the individual-dependent data of the vehicle A Is not related to the vehicle B and cannot be taken over, but the general-purpose data of the vehicle A can be taken over by the vehicle B, so that the operations and settings customized so far can be taken over. Further, the vehicle type dependent data can be taken over as long as the vehicles A and B are the same vehicle type.

Furthermore, when the user B who is a new user uses the used car of the vehicle A, for the private type data, none of the individual-dependent data, the vehicle-type dependent data, and the general-purpose data can be taken over. Any public type data can be inherited.

Such processing makes it possible to properly transfer agent data while protecting privacy.

In addition, the private type data described above is deleted after being stored in the server 12 at the timing immediately before the user gets off the vehicle 11, and is downloaded from the server 12 and used every time the user gets on the vehicle 11. You may do it. By managing in this way, even if the vehicle 11 is stolen, it is possible to prevent the private type data from being stolen.

Furthermore, when borrowing a vehicle 11 as a rental car and renting the same vehicle type as the vehicle 11 that he owns, or when borrowing the vehicle 11 of the same vehicle type determined every time with a share car or the like, the agent By downloading the data from the server 12 and using it, it is possible to use the agent data of the vehicle type-dependent data and the general-purpose data at any time by taking over each time.

In the above description, an example of using the agent to realize the navigation function of the vehicle 11 has been described. However, the object to function as the agent may be other than that.

For example, an agent that realizes a function that outputs a guide voice that assists the speed, accelerator, and brake according to the road condition based on the correlation information between the speed, road inclination, accelerator, and brake and fuel consumption. There may be.

Also set the vehicle's residual value, such as sudden start / brake history, accident history, battery charge count history, travel history (seaside, desert, volcanic ash etc. deterioration factor contact history), dust / odor sensor history, etc. An agent managed as information may be used.

In addition, destination history (anecdotal information such as vehicles ran in Antarctica), rarity of vehicles (number of vehicles traveling in a specific area), driver history (user history: gender, etc. Or the like) may be an agent that manages information that adds value to the vehicle.

Also, the case search results become the added value of the vehicle as it accumulates. Therefore, the accumulated amount of case search results may be visualized information that becomes a quantitative index such as the data amount (... GB retention).

In addition, information such as yen in monetary value may be displayed by comparing the residual value and added value described above with the used vehicles that are distributed in the market.

<Example executed by software>
By the way, the series of processes described above can be executed by hardware, but can also be executed by software. When a series of processing is executed by software, a program constituting the software may execute various functions by installing a computer incorporated in dedicated hardware or various programs. For example, it is installed from a recording medium in a general-purpose personal computer or the like.

FIG. 18 shows a configuration example of a general-purpose personal computer. This personal computer incorporates a CPU (Central Processing Unit) 1001. An input / output interface 1005 is connected to the CPU 1001 via a bus 1004. A ROM (Read Only Memory) 1002 and a RAM (Random Access Memory) 1003 are connected to the bus 1004.

The input / output interface 1005 includes an input unit 1006 including an input device such as a keyboard and a mouse for a user to input an operation command, an output unit 1007 for outputting a processing operation screen and an image of the processing result to a display device, programs, and various types. A storage unit 1008 including a hard disk drive for storing data, a LAN (Local Area Network) adapter, and the like are connected to a communication unit 1009 that executes communication processing via a network represented by the Internet. Also, magnetic disks (including flexible disks), optical disks (including CD-ROM (Compact Disc-Read Only Memory), DVD (Digital Versatile Disc)), magneto-optical disks (including MD (Mini Disc)), or semiconductors A drive 1010 for reading / writing data from / to a removable medium 1011 such as a memory is connected.

The CPU 1001 is read from a program stored in the ROM 1002 or a removable medium 1011 such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, installed in the storage unit 1008, and loaded from the storage unit 1008 to the RAM 1003. Various processes are executed according to the program. The RAM 1003 also appropriately stores data necessary for the CPU 1001 to execute various processes.

In the computer configured as described above, the CPU 1001 loads the program stored in the storage unit 1008 to the RAM 1003 via the input / output interface 1005 and the bus 1004 and executes the program, for example. Is performed.

The program executed by the computer (CPU 1001) can be provided by being recorded on the removable medium 1011 as a package medium, for example. The program can be provided via a wired or wireless transmission medium such as a local area network, the Internet, or digital satellite broadcasting.

In the computer, the program can be installed in the storage unit 1008 via the input / output interface 1005 by attaching the removable medium 1011 to the drive 1010. Further, the program can be received by the communication unit 1009 via a wired or wireless transmission medium and installed in the storage unit 1008. In addition, the program can be installed in advance in the ROM 1002 or the storage unit 1008.

The program executed by the computer may be a program that is processed in time series in the order described in this specification, or in parallel or at a necessary timing such as when a call is made. It may be a program for processing.

In this specification, the system means a set of a plurality of components (devices, modules (parts), etc.), and it does not matter whether all the components are in the same housing. Accordingly, a plurality of devices housed in separate housings and connected via a network and a single device housing a plurality of modules in one housing are all systems. .

Note that the embodiments of the present disclosure are not limited to the above-described embodiments, and various modifications can be made without departing from the scope of the present disclosure.

For example, the present disclosure can take a cloud computing configuration in which one function is shared by a plurality of devices via a network and is processed jointly.

Further, each step described in the above flowchart can be executed by one device or can be shared by a plurality of devices.

Further, when a plurality of processes are included in one step, the plurality of processes included in the one step can be executed by being shared by a plurality of apparatuses in addition to being executed by one apparatus.

In addition, this indication can also take the following structures.
<1> A storage unit that stores learning data in association with a user who uses hardware;
An operation determining unit that determines an operation of the hardware based on the learning data;
A presentation unit that presents options of learning data that can be used by the user among the learning data stored in the storage unit,
The operation determining unit determines an operation based on learning data selected from among the options presented by the presenting unit.
<2> The learning data includes private data depending on the user, and public data other than the private data.
The information processing apparatus according to <1>, wherein the presenting unit presents the public data as an option as learning data that can be used by the operation determining unit among the learning data stored in the storage unit.
<3> The learning data includes individual-dependent data that depends on an individual of the hardware, model-dependent data that depends on a model of the hardware, and general-purpose data that does not depend on the hardware.
The presentation unit is stored in association with the hardware when the user uses another hardware different from the hardware and causes the operation determination unit of the other hardware to determine the operation. The information processing apparatus according to <2>, wherein the general-purpose data among the learning data is presented, and options including private data and public data are presented.
<4> In the case where the hardware and the other hardware are the same model, the presentation unit uses the other hardware different from the hardware so that the user can use the other hardware. Among the learning data stored in association with the other hardware, the model-dependent data and the general-purpose data, and the private data, and The information processing apparatus according to <3>, in which an option including public data is presented.
<5> The presenting unit presents the individual-dependent data, the model-dependent data, and the general-purpose data in the learning data, and presents options to be discarded in units of each private data and public data <4 > The information processing apparatus described in>.
<6> The learning data is configured for each hardware used by the user,
The presenting unit, other options for selecting the hardware, the learning data to be transferred,
Of the learning data stored in the storage unit, public data of learning data learned by the hardware used by the user is presented as an option of learning data that can be used by the operation determination unit of the hardware The information processing apparatus according to <2>.
<7> The learning data is configured for each piece of hardware used by the user, and the individual-dependent data depending on the individual hardware, the model-dependent data depending on the hardware model, and the hardware And general-purpose data that does not depend on
The presenting unit, other options for selecting the hardware, the learning data to be transferred,
Of the learning data stored in the storage unit, general data of learning data learned by the hardware used by the user, which is public data and private data, which is different from the hardware The information processing apparatus according to <2>, wherein the information is presented as an option of learning data that can be used by the operation determination unit of hardware.
<8> The hardware is a vehicle,
The learning data is composed of individual-dependent data that depends on an individual of the vehicle, vehicle-dependent data that depends on four types of the vehicle, and general-purpose data that does not depend on the vehicle,
The individual-dependent data includes repair history, mileage, modification information, collision history, and gasoline remaining amount,
The vehicle type-dependent data includes the fuel consumption, the maximum speed, the information on whether or not the vehicle is allowed to travel on the route to be navigated, the sensing data for detecting the vehicle operation, the automatic driving availability information, and the automatic Including driving routes when driving,
The general-purpose data includes route information, neighboring store information, visit location history, conversation history with agents, driving method, sudden braking, number of horns, presence / absence of smoking, weather, buildings, roads, etc., and external The information processing apparatus according to any one of <1> to <7>, including information.
<9> Based on the learning data, including an anonymization unit that anonymizes the data of the determined operation,
The storage unit stores data determined by the operation determination unit based on the learning data in an anonymized state by the anonymization unit. <1> to <8> The information processing apparatus described.
<10> a storage unit that stores learning data in association with a user who uses hardware;
An operation determining unit that determines an operation of the hardware based on the learning data;
An information processing method of an information processing apparatus including a presentation unit that presents options of learning data that can be used by the user among the learning data stored in the storage unit,
The information processing method includes a step in which the operation determination unit determines an operation based on learning data selected from the options presented by the presentation unit.
<11> A storage unit that stores learning data in association with a user who uses hardware;
An operation determining unit that determines an operation of the hardware based on the learning data;
A presentation unit that presents options of learning data that can be used by the user among the learning data stored in the storage unit,
The operation determining unit is a program for causing a computer to execute a process of determining an operation based on selected learning data among the options presented by the presenting unit.

11, 11-1 to 11-n vehicle, 12 server, 13 network, 31 control unit, 32 vehicle drive unit, 33 operation input unit, 34 vehicle operation detection unit, 35 communication unit, 36 display unit, 37 audio output unit, 38 voice input unit, 39 imaging unit, 40 storage unit, 41 agent processing unit, 61 authentication unit, 62 account management unit, 63 agent data synchronization management unit, 64 agent data usage management unit, 65 agent data migration management unit, 66 agent Data destruction management section, 67 analysis section, 68 case search section, 69 case confirmation section, 70 case correction section, 71 case determination section, 72 case anonymization section, 73 case verification section, 91 control section, 92 communication section, 93 storage Part

Claims

A storage unit that stores learning data in association with a user who uses hardware;
An operation determining unit that determines an operation of the hardware based on the learning data;
A presentation unit that presents options of learning data that can be used by the user among the learning data stored in the storage unit,
The operation determining unit determines an operation based on learning data selected from among the options presented by the presenting unit.
The learning data is composed of private data depending on the user, and public data other than the private data,
The information processing apparatus according to claim 1, wherein the presenting unit presents the public data as an option as learning data that can be used by the operation determining unit among the learning data stored in the storage unit.
The learning data is composed of individual-dependent data that depends on an individual of the hardware, model-dependent data that depends on the model of the hardware, and general-purpose data that does not depend on the hardware,
The presentation unit is stored in association with the hardware when the user uses another hardware different from the hardware and causes the operation determination unit of the other hardware to determine the operation. The information processing apparatus according to claim 2, wherein, among the learning data, the general-purpose data is presented, and options including private data and public data are presented.
When the hardware and the other hardware are the same model, the presentation unit uses the other hardware different from the hardware, and the operation of the other hardware is performed by the user. When determining the operation by the determination unit, among the learning data stored in association with the other hardware, the model-dependent data and the general-purpose data, which are private data and public data, respectively. The information processing apparatus according to claim 3.
The presenting unit presents options to be discarded in units of private data and public data, which are the individual-dependent data, the model-dependent data, and the general-purpose data in the learning data. Information processing device.
The learning data is configured for each piece of hardware used by the user,
The presenting unit, other options for selecting the hardware, the learning data to be transferred,
Of the learning data stored in the storage unit, public data of learning data learned by the hardware used by the user is presented as an option of learning data that can be used by the operation determination unit of the hardware The information processing apparatus according to claim 2.
The learning data is configured for each piece of hardware used by the user and is dependent on the individual of the hardware, dependent on the individual of the hardware, dependent on the model of the hardware, and dependent on the hardware Not composed of general-purpose data,
The presenting unit, other options for selecting the hardware, the learning data to be transferred,
Of the learning data stored in the storage unit, general data of learning data learned by the hardware used by the user, which is public data and private data, which is different from the hardware The information processing apparatus according to claim 2, wherein the information is presented as an option of learning data that can be used by the operation determination unit of hardware.
The hardware is a vehicle,
The learning data is composed of individual-dependent data that depends on an individual of the vehicle, vehicle-dependent data that depends on four types of the vehicle, and general-purpose data that does not depend on the vehicle,
The individual-dependent data includes repair history, mileage, modification information, collision history, and gasoline remaining amount,
The vehicle type-dependent data includes the fuel consumption, the maximum speed, the information on whether or not the vehicle is allowed to travel on the route to be navigated, the sensing data for detecting the vehicle operation, the automatic driving availability information, and the automatic Including driving routes when driving,
The general-purpose data includes route information, neighboring store information, visit location history, conversation history with agents, driving method, sudden braking, number of horns, presence / absence of smoking, weather, buildings, roads, etc., and external The information processing apparatus according to claim 1, comprising information.
Based on the learning data, including the anonymization unit that anonymizes the data of the determined operation,
The information processing apparatus according to claim 1, wherein the storage unit stores the data of the operation determined by the operation determination unit based on the learning data in a state of being anonymized by the anonymization unit.
A storage unit that stores learning data in association with a user who uses hardware;
An operation determining unit that determines an operation of the hardware based on the learning data;
An information processing method of an information processing apparatus including a presentation unit that presents options of learning data that can be used by the user among the learning data stored in the storage unit,
The information processing method includes a step in which the operation determination unit determines an operation based on learning data selected from the options presented by the presentation unit.
A storage unit that stores learning data in association with a user who uses hardware;
An operation determining unit that determines an operation of the hardware based on the learning data;
A presentation unit that presents options of learning data that can be used by the user among the learning data stored in the storage unit,
The operation determining unit is a program for causing a computer to execute a process of determining an operation based on selected learning data among the options presented by the presenting unit.